A Soft Computing Genetic-Neuro fuzzy Approach for Data Mining and Its Application to Medical Diagnosis
Kavita Rawat1, Kavita Burse2
1Kavita Rawat, M. Tech Scolar CSE, R.G.P.V, OCT, Bhopal, India.
2Dr. Kavita Burse, Director OCT, R.G.P.V, OCT, Bhopal, India.
Manuscript received on September 29, 2013. | Revised Manuscript received on October 11, 2013. | Manuscript published on October 30, 2013. | PP: 409-411 | Volume-3, Issue-1, October 2013. | Retrieval Number:  A2309103113/2013©BEIESP

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: A novel way to enhance the performance of a model that combines genetic algorithms and neuro fuzzy logic for feature selection and classification is proposed. This research work involves designing a framework that incorporates genetic algorithm with neuro fuzzy for feature selection and classification on the training dataset. It aims for reducing several medical errors and provides better prediction of diseases. Medical diagnosis of diseases is an important and difficult task, and a proposed method performs feature selection and parameters setting in an evolutionary way. The wrapper approach to feature subset selection is used in this paper because of the accuracy. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the ANFIS classification accuracy. To verify the effectiveness of the proposed approach, it is tested on ovarian cancer dataset.
Keywords: Feature selection, GA, ANFIS, RMSE.